Lectures: Tuesday and Thursday 9:30-10:50 TL 403B

Lab: Wednesday 13:00-14:50 Wachman 104

Schedule and Homework Assignments

- Longin Jan Latecki , email: latecki@temple.edu
- Office: 306 Wachman Hall, phone: 215-204-5781
- Office Hours on skype latecki_tu on Monday 13:00 - 14:00 and Thursday 13:00 - 14:00 or by appointment

- David Dobor, email: david.dobor@gmail.com
- Office: Carnell 1003
- Office Hours: Wednesday 15:00-17:00

The goal is to introduce students to hot and extremely useful topics in computational statistics with hands on experience. It provides a modern approach to probability and computational statistics with applications in data mining. Students will be able to immediately see their results with programming assignments in Matlab. Matlab is a leading programming language of scientific computing. It is broadly used in the industry and academia. No prior Matlab knowledge is required. The course offers a solid foundation for further courses in data mining, machine learning, artificial intelligence, robotics, computer vision, and in general in computational statistics and scientific computing. The course is composed of 3 hours lecture and 2 hours lab with programming assignments in Matlab.

Michael Baron. Probability and Statistics for Computer Scientists, CRC, 2006, ISBN-10: 1584886412

Dekking, F.M., Kraaikamp, C., Lopuhaa, H.P., Meester, L.E., A Modern Introduction to Probability and Statistics. Second Edition. Springer 2007

ISBN: 978-1-85233-896-1

**Also recommended but not required are:**

Wendy L. Martinez and Angel R. Martinez. Computational Statistics Handbook with Matlab. Second Edition. CRC 2008.

Daniel T. Kaplan. Introduction to Scientific Computation and Programming. Thomson 2004.

- Introduction
- Probability Concepts
- Sampling Concepts
- Generating Random Variables
- Exploratory Data Analysis
- Finding Structure in Data
- Monte Carlo Methods in Inferential Statistics
- Data Partitioning
- Probability Density Estimation
- Supervised Learning
- Unsupervised Learning
- Parametric Models
- Nonparametric Models
- Markov Chain Monte Carlo Methods

CIS 1068 (or CIS 1073) and Math 1041 (or Math 1031) with grades of C or better

Questions, email: latecki@temple.edu

Maximum Likelihood Estimation Primer

**Homework:**10%**Matlab Assignments:**10%**Quizzes**: 20%**Class participation**: 5%**Midterm**: 25%**Final**: 30%**Homework:**Late homework will not be accepted.**Class attendance:**Class attendance is expected, and may be recorded from time to time. Absences for legitimate professional activities and illnesses are acceptable only if prior notice is given to the instructor by e-mail or phone. Scheduling conflicts with your work, extra-curricular activities, or any other such activities is**not**a valid excuse. Although attendance is not a specific part of the course evaluation it has a direct effect on class participation. If you are not in class you cannot participate. Class participation means that you attend class regularly and have completed your assigned readings. It means that you ask relevant questions and make informed comments in class. Class participation will contribute to the final grade.**Quizzes:**Each week there will be one 20 minute quiz based on the homework assignment for the previous week. There will be no make up quizzes; however, you will be allowed to drop your lowest three quiz grades. Each quiz will be worth 20pts. You may bring one letter size page filled with your own notes to each quiz.**Exams:**If you miss a midterm for an emergency [as agreed ahead of time with the instructor], there will be no makeup exam: the other exams will become proportionally more important. If you miss any exam without prior agreement, and without definitive proof as to the reasons, you will get a zero. If you miss the final and do not make alternative arrangements before grades are turned in you will be graded F.

- All work submitted for credit must be your own.
- You may discuss the homework problems with your classmates, the teaching assistant, and the instructor. You must acknowledge the people with whom you discussed your work, and you must write up your own solutions and code. Any written sources (apart from the text) used must also be acknowledged; however, you may not consult any solutions from previous years' assignments whether they are student or faculty generated.
- Plagiarism will be handled with severe measures.
- Please ask if you have any questions about the Honor Code. Violations of the honor code will be treated seriously. Please check the Temple University policy on Plagiarism and Academic Cheating.

I encourage students with disabilities, including "invisible" disabilities such as chronic diseases and learning disabilities, to discuss with us any appropriate accommodations that we might make on their behalf. Student must provide me with a note from the office of Disability Resources and Services at in 100 Ritter Annex, 215-204-1280, regarding their disability.